Image search at the major search engines today largely relies on looking at words that are used around images — on the pages that host them, in image file names, and in ALT text associated with them. No real image recognition is done by any of the majors. Search for "apples," and they haven’t actually somehow scanned the images itself to "see" if they contain pictures of apples.

The method in Google’s paper changes that. In short, a group of images retrieved for a query using traditional search methods is then further analyzed. Image recognition software finds which images in the group seem most similar to each other. It then estimates "visual hyperlinks" between them to produce a final ranking.

The last part is important. No actual hyperlinks on the web are used to rank the images, if I understand the paper correctly, other than in the first traditional retrieval process. Instead, the algorithm guesses at how the images would be linked together, with those being most similar having more virtual links to each other. As a result, the most "linked to" images are calculated to rank first.

The image above comes from the paper and shows examples of images found in a search for [mona lisa]. The lines illustrate how they are all estimated to link together, with the two in the middle (as shown in the close-up below) deemed the most relevant based on linkage:

The New York Times article says the researchers call the method "VisualRank," though that term is not used in the actual paper, which is entitled "PageRank for Product Image Search," coming from how the method was applied to product search results as a test. The paper itself talks of Image Rank at one point, so VisualRank might be a new name the researchers are trying out.